Rethinking NLP in the Second Edition of Advanced Data Science and Analytics with Python
When I first wrote Advanced Data Science and Analytics with Python, natural language processing (NLP) occupied a niche corner of the data science landscape. Back then, much of the focus in Python revolved around parsing and vectorising text: extracting tokens, counting frequencies, maybe applying a topic model or two. Fast forward a few years, and NLP has become one of the engines driving modern AI, powering everything from search and recommendation to summarisation and chat interfaces.
That shift is at the heart of Chapter 2 in the second edition, where “Speaking Naturally” has been thoroughly reimagined for today’s ecosystem. Instead of stopping at token counts and bag-of-words, this chapter bridges the gap between traditional text processing and the language-rich representations that underlie contemporary AI systems.
From Soup to Semantics
We start where most real text projects begin, with acquisition and cleaning. Python’s Beautiful Soup still plays a starring role for scraping structured text off the web, but the focus now goes beyond parsing tags to extracting meaningfulcontent. Regular expressions, Unicode normalisation and tokenisation are introduced not as academic subjects but as practical tools you’ll reach for every time you ingest text.
Finding Structure in Language
Once you have clean text, the chapter furthers your intuition with topic modelling, an unsupervised way of surfacing latent themes across documents. These techniques remain valuable for exploration, summarisation and even automated labelling in the absence of annotated training data.
Encoding Meaning: Beyond Frequency Counts
The real leap comes with representation learning. Rather than relying on sparse counts, modern NLP encodes text as dense vectors that capture contextual meaning. Word embeddings — and their contextual successors — turn raw text into numbers that machine learning models can reason about. This edition makes that leap accessible, showing how to generate, visualise and use these representations in Python.
Semantic Search with Vector Engines
Building on embeddings, we explore vector similarity search — the backbone of semantic retrieval. Using tools like FAISS, you’ll learn how to retrieve text not based on matching keywords but on meaning, opening the door to advanced search, clustering and recommendation applications.
The NLP landscape has moved faster than almost any other area of AI. Transformers, contextual language models and embedding systems have shifted what’s possible — and what’s practical — for practitioners. This chapter is carefully redesigned to reflect that evolution, giving you the grounding you need to work with text data that isn’t just cleaned and counted, but understood.
More soon. Stay tuned.